Significant advances in genomics, combinatorial chemistry, and high-throughput screening have created a bottleneck at the lead optimization stage of the drug discovery process. Lead optimization entails the redesign of compounds to impart appropriate absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties. Even after lead optimization, greater than 90 percent of compounds fail in development, and a majority of these failures are due to inadequate ADME/Tox characteristics. Predictive models for ADME/Tox that are based on structure allow for a more rapid redesign process and can result in higher quality drug candidates progressing to clinical trials with optimal ADME/Tox properties. The cytochrome P450 enzymes are the most important enzymes in human drug metabolism. Not only do these enzymes play an important role in determining the half-life of most drugs, they are also involved in many drug-drug interactions and drug-induced toxicities. This proposal centers on predictive models of human drug metabolism mediated by the three most important human cytochrome P450 enzymes, CYPs 3A4, 2C9, and 2D6. Camitro has previously developed and implemented an initial version of such models and is presently validating it in collaboration with pharmaceutical partners. The proposed project involves significant extension and refinement of these models. PROPOSED COMMERCIAL APPLICATION: Computer-based, high-throughput models for the prediction of human metabolism of new drug candidates are urgently needed by the pharmaceutical/biotechnology industry in view of the enormous increase in leads identified by new discovery methodologies such as genomics, combinatorial chemistry, and high-throughput compound screening. Computational models will enable drug discovery scientists to virtually screen compounds prior to their synthesis to rapidly determine their metabolism profiles and to design drug candidates that have optimal, as opposed to merely adequate, metabolism profiles. Compounds thus selected will not only have a greater chance of development success, but are also likely to lead to better medicines with fewer safety concerns.